DESCRIPTION (Applicant's Abstract): The proposed research will investigate the representations of shape that permit us to recognize objects at multiple levels of abstraction. I hypothesize that a primary representation-which is a categorical description of an object's parts and their interrelations-provides abstract and relatively view-invariant information that is useful for determining an object's general class (e.g. a car) and that a secondary representation-which is a more viewpoint-sensitive (and perhaps view-based) metrically-rich representation-provides a basis for determining an object s specific identify (e.g. "my" car). The proposed research will contribute to the growing body of findings supporting the notion of multiple, integrated representation of shape for object recognition. Moreover, the proposed research will introduce a new experimental method for identifying these primary and secondary representations. The proposed research introduces a new framework for studying object recognition at multiple levels of abstraction. Our ability to recognize objects at multiple levels of abstraction is centrally important: recognizing objects as members of a general class but not distinguishing individual instances would be decidedly maladaptive; and recognizing instances out failing to appreciate that different instances may nonetheless belong to a common class would be equally maladaptive. In spite of the importance of our capacity to recognize objects at multiple levels of abstraction, this capacity is currently not will understood, either empirically or theoretically. The results of the proposed experiments will contribute to our understanding of this important capacity, and place important constraints on theories of human object recognition.